Timely understanding of affected areas during disasters is essential for the implementation of emergency response activities. As one of the low-cost and information-rich volunteer geographic information, social media data can reflect geographic events through human behavior, which is a powerful supplementary source for fine-grained flood monitoring in urban areas. However, the value of social media data has not been fully exploited as potential location and water depth information may be embedded in both text and images. In this study, we propose a novel framework for fine-grained information extraction and dynamic spatial-temporal awareness in disaster-stricken areas based on Sina Weibo. First, we construct a novel fine-grained location corpus specifically for urban flooding contexts. The corpus summarizes characteristics of address descriptions in flood-related Weibo texts, including standard address entities and spatial relationship entities, based on the named entity recognition (NER) model. Then, water depth information in texts and images is obtained based on different deep learning modules and fused at the decision level. Specifically, in text analysis module, we summarize and extract diverse descriptions of water depth, and in image analysis module, we develop a water level hierarchical mapping method. Finally, we analyze the spatio-temporal distribution characteristics and variation patterns of the extracted information to enhance situational awareness. Taking the urban flood occurred in Anhui, China as a case study, we find that the variation of flooding hotspot areas in Sina Weibo and rainfall centers show a significant spatial and temporal consistency, and the fusion of text and image-based information can facilitate dynamic perception of flood processes. The framework presented in this study provides a feasible way to implement refined situational awareness and spatio-temporal evolution analysis of urban floods at the city level in time. 

Achieving fine-grained urban flood perception and spatio-temporal evolution analysis based on social media

Emergency management urgently requires comprehensive knowledge while having a high possibility to go beyond individuals' cognitive scope. Therefore, artificial intelligence(AI) supported decision-making under that circumstance is of vital importance. Recent emerging large language models (LLM) provide a new direction for enhancing targeted machine intelligence. However, the utilization of LLM directly would inevitably introduce unreliable output for its inherent issue of hallucination and poor reasoning skills. In this work, we develop a system called Enhancing Emergency decision-making with Knowledge Graph and LLM (E-KELL), which provides evidence-based decision-making in various emergency stages. The study constructs a structured emergency knowledge graph and guides LLMs to reason over it via a prompt chain. In real-world evaluations, E-KELL receives scores of 9.06, 9.09, 9.03, and 9.09 in comprehensibility, accuracy, conciseness, and instructiveness from a group of emergency commanders and firefighters, demonstrating a significant improvement across various situations compared to baseline models. This work introduces a novel approach to providing reliable emergency decision support.

Enhancing Emergency Decision-making with Knowledge Graphs and Large Language Models

Enhancing emergency decision-making with knowledge graphs and large language models

ABSTRACT Urban Functional Zone (UFZ) identification is vital for urban planning, renewal, and development. Point of Interest (POI), as one of the most popular data in UFZ studies, is transformed into a geo-corpus under specific sampling strategies, which can be used with Natural Language Processing (NLP) technology to extract geo-semantic features and identify UFZs. However, existing studies only capture a single spatial distribution pattern of POIs, while ignoring the other spatial distribution information. In this paper, we developed an integrated geo-corpus construction approach to capture multi-spatial distribution patterns of POIs that were represented by different modal POI embeddings. Subsequently, random forest model was leveraged to classify UFZs based on those embeddings. A set of combination experiments were designed for performance validation. The results show that our proposed method can effectively identify UFZs with an accuracy of 72.9%, with an improvement of 8.5% compared to the baseline methods. The outcome of this study will help urban planners to better understand UFZs through investigating the integrated spatial distribution patterns of POIs embedded in UFZs.

https://doi.org/10.1080/17538947.2022.2160841

Identifying urban functional zones by capturing multi-spatial distribution patterns of points of interest

ABSTRACT Extracting boundaries and recognizing features of metro station catchment area (MSCA) are crucial for rational urban and transportation planning. Metro stations characterized by their high concentration of people and activities, serve as key locations for human-environment interaction and the development of cognitive relationships with individuals. Traditional research on MSCA has largely relied on distance, based estimations, often overlooking the cognitive influences. This study introduces a novel framework for the extraction of MSCA boundaries and the recognition of significant features. It examines the relationship between Points of Interest (POIs) and metro stations from two pivotal dimensions: cognition and perception. Cognitive relevance is evaluated through web co-occurrence analyses and combined effects, whereas perceptual relevance is assessed by simulating pedestrian perceptions using a cumulative Gaussian function. Utilizing the highly relevant POIs and road network data, the Alpha shape algorithm is deployed and combined with road data to delineate boundaries. Furthermore, Weighted Kernel Density Estimation is employed to recognize features and their distribution. The results show that our framework can well represent MSCA boundaries and features. Such an approach enhances the understanding of human-environment interactions in metro-centric areas and offers a data-driven approach for optimizing urban layouts, improving land use, and promoting transit-oriented development.

Boundaries extraction and features recognition of metro station catchment area: the perspective of cognition and perception

Abstract. Natural disasters have a significant impact on the environment and economies of all countries around the world, and a large amount of multi-source heterogeneous geographic information data is generated every day. However, due to a lack of knowledge transformation capabilities, these nations continue to struggle with the issue of "a large amount of data and little knowledge". Therefore, it is of great significance how to extract geographic knowledge related to disasters from the vast data and construct a geographic knowledge graph integrating disaster information. Based on the theory related to knowledge extraction, this paper proposes a method to construct a natural disaster knowledge graph integrating geographic information. The core of this knowledge graph is to construct the association relationship between natural disaster concepts, research areas, and spatial data. The vocabulary and relationships associated with disaster concepts are primarily transformed by an existing word list of geographic narratives, which then provide rich semantic relationships of domain concepts for the entire knowledge graph. The research areas and spatial data types are mainly obtained through knowledge entity extraction and disambiguation methods. This disaster knowledge graph can support applications well such as natural disaster visualization and analysis, data recommendation systems, and intelligent Q&A systems, which can further improve the intelligence of natural disaster knowledge services and is expected to promote the sharing and reuse of domain knowledge graphs to a certain extent.


/pdf/research-on-the-construction-of-geographic-knowledge-graph-1wllgj32.pdf

Research on the construction of geographic knowledge graph integrating natural disaster information

Abstract. The accelerated urbanisation, threatening the integrity of ecological environment. The lack of future simulation in ecological risk assessment in current studies. Especially in metropolis, to address this problem, this study uses the Multiple objective programming (MOP) and the improved patch-generating land use simulation (PLUS) models to simulate land use in Beijing in 2035 under the Natural Development (ND) scenario and the Liveable City (LC) scenario; the changes in land types and land transfers under different scenarios are analysed. In addition, a landscape ecological risk assessment method was applied to analyse the ecological risks caused by land use in each scenario. The study found that, in general, the dominant trend of land use change in Beijing is the shift from construction land to ecological land. The spatial pattern of ecological risk is polarized from east to west, and the conversion of a large amount of built-up land to arable land or grassland, and the high vulnerability of arable land and grassland to human destruction, are the reasons for the continuous increase in ecological risk in Beijing. The area of construction land in the LC scenario is closer to the planned area than in the ND scenario, and the ecological risks faced by the LC scenario are slightly lower than those of the ND scenario. Therefore, it is reasonable to use the LC scenario to simulate the land use situation in Beijing in 2035.


/pdf/simulation-of-ecological-risk-in-beijing-using-mop-plus-2ve67vmx.pdf

Simulation of ecological risk in beijing using mop-plus model

Abstract. Classical Chinese gardens built in scenic areas have characteristics in rich scales, multi-type elements, and a complex development history. Due to the designed landscape characteristics of "hidden the garden in bigger landscapes", it is often difficult to obtain high-quality heritage information through traditional surveying approaches. The aim of this research is to establish a digital 3D documentation technology system for the classical gardens in scenic areas. Suiyuan Garden, a classical Chinese garden with a history of more than 300 years, was used as a case study. We have integrated UAV low-altitude photogrammetry, handheld laser scanning, ground-mounted laser scanning, macro structured light scanning and other technologies to obtain the 3D data, and to build a digital twin by developing the technical workflow and data standard. The innovation of the research lies in: 1) We used a variety of digital mapping methods to record the pattern between the garden and the surrounding landscape; 2) We adopted a combination of handheld and ground-mounted laser scanning systems to build spatial structures and better cover the ground spaces; 3) We established a workflow for the conversion from point cloud to mesh model for different garden features, which can improve the usability of 3D heritage information; 4) We used the 3D cultural heritage modelling approach to improve the standards of the digital conservation of historic plants. The outcomes of this research could be used as the basis for the construction of Historic Landscape Information Model (HLIM) in the near future.


https://doi.org/10.5194/isprs-archives-xlviii-m-2-2023-1659-2023

Research on the 3d documentation system of classical chinese gardens in scenic areas – taking kunshan suiyuan as an example

Abstract. Urban Functional Zone (UFZ) identification facilitates the understanding of urban systems, which are complex and huge, and helps promote sustainable urban development. Existing studies on UFZ identification with Points of Interests (POIs) have focused much on more accurately extracting functional semantics, but ignored the fine delineation of UFZs in the spatial domain. The fine delineation of the spatial units of UFZs is also a key issue in UFZ identification. Since the sizes of UFZs can be different in practice, it is difficult to delineate spatially heterogeneous UFZs on a fixed scale. To solve the issue, a novel multi-scale spatial segmentation method was proposed in this study. Through taking the homogeneous socio-economic attributes of UFZs into account, we firstly generated a number of multi-scale spatial units by computing the mixed degree of POIs types, which reflects the mixed functions of each UFZs, using information entropy. Subsequently, we constructed the urban functional corpus of each spatial unit by measuring the spatial distribution pattern of POIs. The Word2Vec model was employed to obtain the semantic embedding vectors of UFZs, following which we adopted cosine distance-based K-means clustering method to group similar UFZs into one cluster. Finally, the enrichment factor was used to help annotate each functional cluster with a specific label. The UFZ identification results were compared with the Baidu e-maps and Baidu street view images for evaluation, and an accuracy of 82.7% was obtained. This study considering the heterogeneous distribution of POIs supports the fine-grained identification of UFZs, providing reference for urban planning.


Urban functional zone identification by considering the heterogeneous distribution of points of interests

Abstract. The analysis of natural hazards is still lacking in real-time and comprehensiveness. To enhance the intelligent analysis of disasters, reduce natural disasters, and provide more timely and accurate disaster warnings. The method of constructing a natural disaster research event graph using the abstracts of research literature related to natural disaster analysis as the data source is proposed. First, a rule-based matching algorithm combined with syntactic analysis is used to extract the events of the disaster analysis process and spatio-temporal information from the abstract; Then build an event storage model based on Neo4j to store the event chain and related information; Then use Word2vec to convert events into word vectors, and define the event similarity as a linear sum of the object similarity and predicate similarity of the two events, combine event similarity setting equation to calculate core nodes and perform event generalization and fusion of graphs. Based on the above method, soil erosion is used as an example to construct an event graph and provide the basis for decision making services for intelligent analysis of natural disasters.


/pdf/event-graph-construction-method-on-natural-disaster-research-2h9dv7si.pdf

Event graph construction method on natural disaster research

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